# RepTate: Rheology of Entangled Polymers: Toolkit for the Analysis of Theory and Experiments
# --------------------------------------------------------------------------------------------------------
#
# Authors:
# Jorge Ramirez, jorge.ramirez@upm.es
# Victor Boudara, victor.boudara@gmail.com
#
# Useful links:
# http://blogs.upm.es/compsoftmatter/software/reptate/
# https://github.com/jorge-ramirez-upm/RepTate
# http://reptate.readthedocs.io
#
# --------------------------------------------------------------------------------------------------------
#
# Copyright (2017-2023): Jorge Ramirez, Victor Boudara, Universidad Politécnica de Madrid, University of Leeds
#
# This file is part of RepTate.
#
# RepTate is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RepTate is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with RepTate. If not, see <http://www.gnu.org/licenses/>.
#
# --------------------------------------------------------------------------------------------------------
"""Module ApplicationReact
React module
"""
from RepTate.gui.QApplicationWindow import QApplicationWindow
from RepTate.core.View import View
from RepTate.core.FileType import TXTColumnFile
import numpy as np
[docs]
class ApplicationReact(QApplicationWindow):
"""Application for Monte Carlo polymerisation"""
appname = 'React'
description = 'React Application' #used in the command-line Reptate
extension = 'reac'
html_help_file = 'http://reptate.readthedocs.io/manual/Applications/React/React.html'
def __init__(self, name='React', parent=None, **kwargs):
"""**Constructor**"""
from RepTate.theories.TheoryLDPEBatch import TheoryTobitaBatch
from RepTate.theories.TheoryTobitaCSTR import TheoryTobitaCSTR
from RepTate.theories.TheoryMultiMetCSTR import TheoryMultiMetCSTR
from RepTate.theories.TheoryReactMix import TheoryReactMix
from RepTate.theories.TheoryCreatePolyconf import TheoryCreatePolyconf
from RepTate.theories.TheoryDieneCSTR import TheoryDieneCSTR
super().__init__(name, parent)
# VIEWS
# set the views that can be selected in the view combobox
self.views["w(M)"] = View(
name="w(M)",
description="Molecular weight distribution",
x_label="M",
y_label="w",
x_units="g/mol",
y_units="-",
log_x=True,
log_y=False,
view_proc=self.view_wM,
n=1,
snames=["w"])
self.views["g(M)"] = View(
name="g(M)",
description="g(M)",
x_label="M",
y_label="g(M)",
x_units="g/mol",
y_units="-",
log_x=True,
log_y=False,
view_proc=self.view_gM,
n=1,
snames=["g(M)"])
self.views['br/1000C'] = View(
name="br/1000C",
description="br/1000C(M)",
x_label="M",
y_label="br/1000C",
x_units="g/mol",
y_units="-",
log_x=True,
log_y=False,
view_proc=self.view_br_1000C,
n=1,
snames=["br/1000C"])
self.views["log(w(M))"] = View(
name="log(w(M))",
description="Molecular weight distribution",
x_label="M",
y_label="log(w)",
x_units="g/mol",
y_units="-",
log_x=True,
log_y=False,
view_proc=self.view_logwM,
n=1,
snames=["log(w)"])
self.views["log(g(M))"] = View(
name="log(g(M))",
description="log(g(M))",
x_label="M",
y_label="log(g)",
x_units="g/mol",
y_units="-",
log_x=True,
log_y=False,
view_proc=self.view_loggM,
n=1,
snames=["log(g)"])
self.views['p(mass br) log-lin'] = View(
name="p(mass br) log-lin",
description="Prob. dist. of mass segement b/w branch pt log-lin scale",
x_label="M segment",
y_label="p(M segment)",
x_units="g/mol",
y_units="-",
log_x=True,
log_y=False,
view_proc=self.thview_proba_mass_br,
n=1,
snames=["p(M segment)"],
with_thline=False)
self.views['p(br/molecule) log-lin'] = View(
name="p(br/molecule) log-lin",
description="Prob. dist. of num. branch pt per molecule log-lin scale",
x_label="Num. br/molecule",
y_label="p(br/molecule)",
x_units="-",
y_units="-",
log_x=True,
log_y=False,
view_proc=self.thview_proba_num_br,
n=1,
snames=["p(M segment)"],
with_thline=False)
self.views['p(br/molecule) lin-lin'] = View(
name="p(br/molecule) lin-lin",
description="Prob. dist. of num. branch pt per molecule lin-lin scale",
x_label="Num. br/molecule",
y_label="p(br/molecule)",
x_units="-",
y_units="-",
log_x=False,
log_y=False,
view_proc=self.thview_proba_num_br,
n=1,
snames=["p(M segment)"],
with_thline=False)
#### extra views for P&S:
self.views['<senio(prio)> log-log'] = View(
name="<senio(prio)> log-log",
description="Average seniority vs priority log-log scale",
x_label="Priority",
y_label="Average Seniority",
x_units="-",
y_units="-",
log_x=True,
log_y=True,
view_proc=self.thview_avsenio_v_prio,
n=3,
snames=["av_senio"],
with_thline=False)
self.views['<senio(prio)> lin-log'] = View(
name="<senio(prio)> lin-log",
description="Average seniority vs priority lin-log scale",
x_label="Priority",
y_label="Average Seniority",
x_units="-",
y_units="-",
log_x=False,
log_y=True,
view_proc=self.thview_avsenio_v_prio,
n=3,
snames=["av_senio"],
with_thline=False)
self.views['<senio(prio)> lin-lin'] = View(
name="<senio(prio)> lin-lin",
description="Average seniority vs priority lin-lin scale",
x_label="Priority",
y_label="Average Seniority",
x_units="-",
y_units="-",
log_x=False,
log_y=False,
view_proc=self.thview_avsenio_v_prio,
n=3,
snames=["av_senio"],
with_thline=False)
#####
self.views['<prio(senio)> log-log'] = View(
name="<prio(senio)> log-log",
description="Average priority vs seniority log-log scale",
x_label="Seniority",
y_label="Average Priority",
x_units="-",
y_units="-",
log_x=True,
log_y=True,
view_proc=self.thview_avprio_v_senio,
n=3,
snames=["av_prio"],
with_thline=False)
self.views['<prio(senio)> lin-log'] = View(
name="<prio(senio)> lin-log",
description="Average priority vs seniority lin-log scale",
x_label="Seniority",
y_label="Average Priority",
x_units="-",
y_units="-",
log_x=False,
log_y=True,
view_proc=self.thview_avprio_v_senio,
n=3,
snames=["av_prio"],
with_thline=False)
self.views['<prio(senio)> lin-lin'] = View(
name="<prio(senio)> lin-lin",
description="Average priority vs seniority lin-lin scale",
x_label="Seniority",
y_label="Average Priority",
x_units="-",
y_units="-",
log_x=False,
log_y=False,
view_proc=self.thview_avprio_v_senio,
n=3,
snames=["av_prio"],
with_thline=False)
#####
self.views['p(senio) log-log'] = View(
name="p(senio) log-log",
description="seniority prob. dist. log-log",
x_label="Seniority",
y_label="Probability",
x_units="-",
y_units="-",
log_x=True,
log_y=True,
view_proc=self.thview_proba_senio,
n=1,
snames=["proba_senio"],
with_thline=False)
self.views['p(senio) lin-log'] = View(
name="p(senio) lin-log",
description="seniority prob. dist. lin-log scale",
x_label="Seniority",
y_label="Probability",
x_units="-",
y_units="-",
log_x=False,
log_y=True,
view_proc=self.thview_proba_senio,
n=1,
snames=["proba_senio"],
with_thline=False)
#####
self.views['p(prio) log-log'] = View(
name="p(prio) log-log",
description="Priority prob. dist. log-log scale",
x_label="Priority",
y_label="Probability",
x_units="-",
y_units="-",
log_x=True,
log_y=True,
view_proc=self.thview_proba_prio,
n=1,
snames=["proba_prio"],
with_thline=False)
self.views['p(prio) lin-log'] = View(
name="p(prio) lin-log",
description="Priority prob. dist. lin-log scale",
x_label="Priority",
y_label="Probability",
x_units="-",
y_units="-",
log_x=False,
log_y=True,
view_proc=self.thview_proba_prio,
n=1,
snames=["proba_prio"],
with_thline=False)
####
self.views['<mass br(senio)> log-log'] = View(
name="<mass br(senio)> log-log",
description="Average mol. mass b/w branch pt vs seniority log-log scale",
x_label="Seniority",
y_label="Av. strand length",
x_units="-",
y_units="g/mol",
log_x=True,
log_y=True,
view_proc=self.thview_avarmlen_v_senio,
n=1,
snames=["av_strand_length"],
with_thline=False)
self.views['<mass br(senio)> lin-lin'] = View(
name="<mass br(senio)> lin-lin",
description="Average mol. mass b/w branch pt vs seniority lin-lin scale",
x_label="Seniority",
y_label="Av. strand length",
x_units="-",
y_units="g/mol",
log_x=False,
log_y=False,
view_proc=self.thview_avarmlen_v_senio,
n=1,
snames=["av_strand_length"],
with_thline=False)
####
self.views['<mass br(prio)> log-log'] = View(
name="<mass br(prio)> log-log",
description="Average mol. mass b/w branch pt vs priority log-log scale",
x_label="Priority",
y_label="Av. strand length",
x_units="-",
y_units="g/mol",
log_x=True,
log_y=True,
view_proc=self.thview_avarmlen_v_prio,
n=1,
snames=["av_strand_length"],
with_thline=False)
self.views['<mass br(prio)> lin-lin'] = View(
name="<mass br(prio)> lin-lin",
description="Average mol. mass b/w branch pt vs priority lin-lin scale",
x_label="Priority",
y_label="Av. strand length",
x_units="-",
y_units="g/mol",
log_x=False,
log_y=False,
view_proc=self.thview_avarmlen_v_prio,
n=1,
snames=["av_strand_length"],
with_thline=False)
self.extra_view_names = [
'<senio(prio)> log-log', '<senio(prio)> lin-log', '<senio(prio)> lin-lin',
'<prio(senio)> log-log', '<prio(senio)> lin-log', '<prio(senio)> lin-lin',
'p(senio) log-log', 'p(senio) lin-log',
'p(prio) log-log', 'p(prio) lin-log',
'<mass br(senio)> log-log', '<mass br(senio)> lin-lin',
'<mass br(prio)> log-log', '<mass br(prio)> lin-lin'
]
#set multiviews
self.nplots = 3
self.multiviews = []
for i in range(self.nplot_max):
# set views in the same order as declared above
self.multiviews.append(list(self.views.values())[i])
self.multiplots.reorg_fig(self.nplots)
# FILES
# set the type of files that ApplicationReact can open
ftype = TXTColumnFile(
name='React files',
extension='reac',
description='Reatc file',
col_names=['M', 'w(M)', 'g', 'br/1000C'],
basic_file_parameters=[],
col_units=['g/mol', '-', '-', '-'])
self.filetypes[
ftype.extension] = ftype #add each the file type to dictionary
# THEORIES
# add the theories related to ApplicationReact to the dictionary, e.g.:
self.theories[TheoryTobitaBatch.thname] = TheoryTobitaBatch
self.theories[TheoryTobitaCSTR.thname] = TheoryTobitaCSTR
self.theories[TheoryMultiMetCSTR.thname] = TheoryMultiMetCSTR
self.theories[TheoryReactMix.thname] = TheoryReactMix
self.theories[TheoryCreatePolyconf.thname] = TheoryCreatePolyconf
self.theories[TheoryDieneCSTR.thname] = TheoryDieneCSTR
self.add_common_theories()
#set the current view
self.set_views()
# for _ in self.extra_view_names:
# self.viewComboBox.removeItem(self.viewComboBox.count() - 1)
#add the GUI-specific objects here:
for _ in self.extra_view_names:
self.viewComboBox.removeItem(self.viewComboBox.count() - 1)
[docs]
def change_view(self):
"""Redefinition to handle the x-range selection when P&S is selected"""
do_priority_seniority = False
try:
ds = self.DataSettabWidget.currentWidget()
th = ds.TheorytabWidget.currentWidget()
do_priority_seniority = th.do_priority_seniority
except Exception as e:
pass
super().change_view(x_vis=do_priority_seniority)
[docs]
def view_wM(self, dt, file_parameters):
"""Molecular weight distribution :math:`w(M)` vs molecular weight :math:`M` (in logarithmic scale)
"""
x = np.zeros((dt.num_rows, 1))
y = np.zeros((dt.num_rows, 1))
x[:, 0] = dt.data[:, 0]
y[:, 0] = dt.data[:, 1]
return x, y, True
[docs]
def view_logwM(self, dt, file_parameters):
"""Logarithm of the molecular weight distribution :math:`\\log(w(M))` vs molecular weight :math:`M` (in logarithmic scale)
"""
x = np.zeros((dt.num_rows, 1))
y = np.zeros((dt.num_rows, 1))
x[:, 0] = dt.data[:, 0]
y[:, 0] = np.log10(dt.data[:, 1])
return x, y, True
[docs]
def view_gM(self, dt, file_parameters):
""":math:`g`-factor as a function of the molecular weight.
The :math:`g`-factor is defined as :math:`g = \\dfrac{\\langle R^2_g \\rangle_\\text{branched}}{\\langle R^2_g \\rangle_\\text{linear}}`
"""
x = np.zeros((dt.num_rows, 1))
y = np.zeros((dt.num_rows, 1))
x[:, 0] = dt.data[:, 0]
y[:, 0] = dt.data[:, 2]
return x, y, True
[docs]
def view_loggM(self, dt, file_parameters):
"""Logarithm of the :math:`g`-factor as a function of the molecular weight.
The :math:`g`-factor is defined as :math:`g = \\dfrac{\\langle R^2_g \\rangle_\\text{branched}}{\\langle R^2_g \\rangle_\\text{linear}}`
"""
x = np.zeros((dt.num_rows, 1))
y = np.zeros((dt.num_rows, 1))
x[:, 0] = dt.data[:, 0]
y[:, 0] = np.log10(dt.data[:, 2])
return x, y, True
[docs]
def view_br_1000C(self, dt, file_parameters):
"""Number of branching points per 1000 carbon as a function of the molecular weight
"""
x = np.zeros((dt.num_rows, 1))
y = np.zeros((dt.num_rows, 1))
x[:, 0] = dt.data[:, 0]
y[:, 0] = dt.data[:, 3]
return x, y, True
[docs]
def thview_avprio_v_senio(self, dt, file_parameters):
try:
data = dt.extra_tables['avprio_v_senio']
except:
x = np.zeros((1, 3))
y = np.zeros((1, 3))
y[:] = np.nan
return x, y, True
nrows = len(data[:, 0])
x = np.zeros((nrows, 3))
y = np.zeros((nrows, 3))
maxp = np.nanmax(data[:, 1])
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
# comb limit
x[:, 1] = data[:, 0]
y[:, 1] = data[:, 0]
# Cayley tree limit
x[:, 2] = data[:, 0]
y[:, 2] = np.power(2, data[:, 0] - 1)
# avoid large numbers
y[:, 2] = np.where(y[:, 2] <= maxp , y[:, 2], np.nan)
return x, y, True
[docs]
def thview_avsenio_v_prio(self, dt, file_parameters):
try:
data = dt.extra_tables['avsenio_v_prio']
except:
x = np.zeros((1, 3))
y = np.zeros((1, 3))
y[:] = np.nan
return x, y, True
nrows = len(data[:, 0])
x = np.zeros((nrows, 3))
y = np.zeros((nrows, 3))
maxs = np.nanmax(data[:, 1])
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
# comb limit
x[:, 1] = data[:, 0]
y[:, 1] = data[:, 0]
# Cayley tree limit
x[:, 2] = data[:, 0]
y[:, 2] = np.log(data[:, 0]) / np.log(2) + 1
# avoid large numbers
y[:, 1] = np.where(y[:, 1] <= maxs , y[:, 1], np.nan)
return x, y, True
[docs]
def thview_proba_prio(self, dt, file_parameters):
try:
data = dt.extra_tables['proba_prio']
is_extra = True
except:
is_extra = False
if is_extra:
nrows = len(data[:, 0])
x = np.zeros((nrows, 1))
y = np.zeros((nrows, 1))
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
else:
x = np.zeros((1, 1))
y = np.zeros((1, 1))
y[:] = np.nan
return x, y, True
[docs]
def thview_proba_senio(self, dt, file_parameters):
try:
data = dt.extra_tables['proba_senio']
is_extra = True
except:
is_extra = False
if is_extra:
nrows = len(data[:, 0])
x = np.zeros((nrows, 1))
y = np.zeros((nrows, 1))
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
else:
x = np.zeros((1, 1))
y = np.zeros((1, 1))
y[:] = np.nan
return x, y, True
[docs]
def thview_avarmlen_v_prio(self, dt, file_parameters):
try:
data = dt.extra_tables['avarmlen_v_prio']
is_extra = True
except:
is_extra = False
if is_extra:
nrows = len(data[:, 0])
x = np.zeros((nrows, 1))
y = np.zeros((nrows, 1))
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
else:
x = np.zeros((1, 1))
y = np.zeros((1, 1))
y[:] = np.nan
return x, y, True
[docs]
def thview_avarmlen_v_senio(self, dt, file_parameters):
try:
data = dt.extra_tables['avarmlen_v_senio']
is_extra = True
except:
is_extra = False
if is_extra:
nrows = len(data[:, 0])
x = np.zeros((nrows, 1))
y = np.zeros((nrows, 1))
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
else:
x = np.zeros((1, 1))
y = np.zeros((1, 1))
y[:] = np.nan
return x, y, True
[docs]
def thview_proba_mass_br(self, dt, file_parameters):
try:
data = dt.extra_tables['proba_arm_wt']
is_extra = True
except:
is_extra = False
if is_extra:
nrows = len(data[:, 0])
x = np.zeros((nrows, 1))
y = np.zeros((nrows, 1))
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
else:
x = np.zeros((1, 1))
y = np.zeros((1, 1))
y[:] = np.nan
return x, y, True
[docs]
def thview_proba_num_br(self, dt, file_parameters):
try:
data = dt.extra_tables['proba_br_pt']
is_extra = True
except:
is_extra = False
if is_extra:
nrows = len(data[:, 0])
x = np.zeros((nrows, 1))
y = np.zeros((nrows, 1))
x[:, 0] = data[:, 0]
y[:, 0] = data[:, 1]
else:
x = np.zeros((1, 1))
y = np.zeros((1, 1))
y[:] = np.nan
return x, y, True
